library(tidyverse)
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library(lubridate)
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##     date, intersect, setdiff, union
time_series_confirmed_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv")) %>%
  rename(Province_State = "Province/State", Country_Region = "Country/Region")  %>% 
               pivot_longer(-c(Province_State, Country_Region, Lat, Long),
                             names_to = "Date", values_to = "Confirmed")
## Parsed with column specification:
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##   .default = col_double(),
##   `Province/State` = col_character(),
##   `Country/Region` = col_character()
## )
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Let’s get the times series data for deaths

time_series_deaths_long <- read_csv(url("https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv")) %>%
  rename(Province_State = "Province/State", Country_Region = "Country/Region")  %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long),
               names_to = "Date", values_to = "Deaths")
## Parsed with column specification:
## cols(
##   .default = col_double(),
##   `Province/State` = col_character(),
##   `Country/Region` = col_character()
## )
## See spec(...) for full column specifications.

Create Keys

time_series_confirmed_long <- time_series_confirmed_long %>% 
  unite(Key, Province_State, Country_Region, Date, sep = ".", remove = FALSE)
time_series_deaths_long <- time_series_deaths_long %>% 
  unite(Key, Province_State, Country_Region, Date, sep = ".") %>% 
  select(Key, Deaths)

Join tables

time_series_long_joined <- full_join(time_series_confirmed_long,
    time_series_deaths_long, by = c("Key")) %>% 
    select(-Key)

Reformat the data

time_series_long_joined$Date <- mdy(time_series_long_joined$Date)

Create Report table with counts

time_series_long_joined_counts <- time_series_long_joined %>% 
  pivot_longer(-c(Province_State, Country_Region, Lat, Long, Date),
               names_to = "Report_Type", values_to = "Counts")

Plot graph to a pdf outputfile

pdf("images/time_series_example_plot.pdf", width=6, height=3)
time_series_long_joined %>% 
  group_by(Country_Region,Date) %>% 
  summarise_at(c("Confirmed", "Deaths"), sum) %>% 
  filter (Country_Region == "US") %>% 
    ggplot(aes(x = Date,  y = Deaths)) + 
    geom_point() +
    geom_line() +
    ggtitle("US COVID-19 Deaths")
dev.off()
## png 
##   2

Plot graph to a png outputfile

ppi <- 300
png("images/time_series_example_plot.png", width=6*ppi, height=6*ppi, res=ppi)
time_series_long_joined %>% 
  group_by(Country_Region,Date) %>% 
  summarise_at(c("Confirmed", "Deaths"), sum) %>% 
  filter (Country_Region == "US") %>% 
    ggplot(aes(x = Date,  y = Deaths)) + 
    geom_point() +
    geom_line() +
    ggtitle("US COVID-19 Deaths")
dev.off()
## png 
##   2

This is the RMarkdown style for inserting images

Your image must be in your working directory

This command is put OUTSIDE the r code chunk

US COVID-19 Deaths

Interactive Graphs

library(plotly)
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ggplotly(
  time_series_long_joined %>% 
    group_by(Country_Region,Date) %>% 
    summarise_at(c("Confirmed", "Deaths"), sum) %>% 
    filter (Country_Region == "US") %>% 
    ggplot(aes(x = Date,  y = Deaths)) + 
      geom_point() +
      geom_line() +
      ggtitle("US COVID-19 Deaths")
 )

# Subset the time series data to include US deaths

US_deaths <- time_series_long_joined %>% 
    group_by(Country_Region,Date) %>% 
    summarise_at(c("Confirmed", "Deaths"), sum) %>% 
    filter (Country_Region == "US")

Collect the layers for agraph of the US time series data for covid deaths

 p <- ggplot(data = US_deaths, aes(x = Date,  y = Deaths)) + 
        geom_point() +
        geom_line() +
        ggtitle("US COVID-19 Deaths")

Plot the graph using ggplotly

ggplotly(p)
library(gganimate)
library(gifski)
library(transformr)
theme_set(theme_bw())

An animation of the confirmed cases in select countries

data_time <- time_series_long_joined %>% 
    group_by(Country_Region,Date) %>% 
    summarise_at(c("Confirmed", "Deaths"), sum) %>% 
    filter (Country_Region %in% c("China","Korea, South","Japan","Italy","US"))
p <- ggplot(data_time, aes(x = Date,  y = Confirmed, color = Country_Region)) + 
      geom_point() +
      geom_line() +
      ggtitle("Confirmed COVID-19 Cases") +
      geom_point(aes(group = seq_along(Date))) +
      transition_reveal(Date) 
animate(p, end_pause = 15)